## Test Differences Between Category Means

This example shows how to test for significant differences
between category (group) means using a *t*-test, two-way ANOVA
(analysis of variance), and ANOCOVA (analysis of covariance) analysis.

The goal is determining if the expected miles per gallon for a car depends on the decade in which it was manufactured, or the location where it was manufactured.

**Note**

The `nominal`

and `ordinal`

array data types are not recommended. To represent ordered and unordered discrete, nonnumeric
data, use the Categorical Arrays data type instead.

### Load sample data.

```
load('carsmall')
unique(Model_Year)
```

ans = 70 76 82

The variable `MPG`

has miles per gallon measurements on a
sample of 100 cars. The variables `Model_Year`

and
`Origin`

contain the model year and country of origin for
each car.

The first factor of interest is the decade of manufacture. There are three manufacturing years in the data.

### Create a factor for the decade of manufacture.

Create an ordinal array named `Decade`

by merging the
observations from years `70`

and `76`

into a
category labeled `1970s`

, and putting the observations from
`82`

into a category labeled
`1980s`

.

Decade = ordinal(Model_Year,{'1970s','1980s'},[],[70 77 82]); getlevels(Decade)

ans = 1970s 1980s

### Plot data grouped by category.

Draw a box plot of miles per gallon, grouped by the decade of manufacture.

```
figure()
boxplot(MPG,Decade)
title('Miles per Gallon, Grouped by Decade of Manufacture')
```

The box plot suggests that miles per gallon is higher in cars manufactured during the 1980s compared to the 1970s.

### Compute summary statistics.

Compute the mean and variance of miles per gallon for each decade.

[xbar,s2,grp] = grpstats(MPG,Decade,{'mean','var','gname'})

xbar = 19.7857 31.7097 s2 = 35.1429 29.0796 grp = '1970s' '1980s'

This output shows that the mean miles per gallon in the 1980s was
`31.71`

, compared to `19.79`

in the 1970s.
The variances in the two groups are similar.

### Conduct a two-sample t-test for equal group means.

Conduct a two-sample *t*-test, assuming equal variances, to
test for a significant difference between the group means. The hypothesis is

$$\begin{array}{l}{H}_{0}:{\mu}_{70}={\mu}_{80}\\ {H}_{A}:{\mu}_{70}\ne {\mu}_{80}.\end{array}$$

MPG70 = MPG(Decade=='1970s'); MPG80 = MPG(Decade=='1980s'); [h,p] = ttest2(MPG70,MPG80)

h = 1 p = 3.4809e-15

`1`

indicates the null hypothesis is rejected at
the default 0.05 significance level. The p-value for the test is very small.
There is sufficient evidence that the mean miles per gallon in the 1980s differs
from the mean miles per gallon in the 1970s.### Create a factor for the location of manufacture.

The second factor of interest is the location of manufacture. First, convert
`Origin`

to a nominal
array.

Location = nominal(Origin); tabulate(Location)

tabulate(Location) Value Count Percent France 4 4.00% Germany 9 9.00% Italy 1 1.00% Japan 15 15.00% Sweden 2 2.00% USA 69 69.00%

### Merge categories.

Combine the categories `France`

, `Germany`

,
`Italy`

, and `Sweden`

into a new category
named `Europe`

.

Location = mergelevels(Location, ... {'France','Germany','Italy','Sweden'},'Europe'); tabulate(Location)

Value Count Percent Japan 15 15.00% USA 69 69.00% Europe 16 16.00%

### Compute summary statistics.

Compute the mean miles per gallon, grouped by the location of manufacture.

[xbar,grp] = grpstats(MPG,Location,{'mean','gname'})

xbar = 31.8000 21.1328 26.6667 grp = 'Japan' 'USA' 'Europe'

This result shows that average miles per gallon is lowest for the sample of cars manufactured in the U.S.

### Conduct two-way ANOVA.

Conduct a two-way ANOVA to test for differences in expected miles per gallon
between factor levels for `Decade`

and
`Location`

.

The statistical model is

$$MP{G}_{ij}=\mu +{\alpha}_{i}+{\beta}_{j}+{\epsilon}_{ij},\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}i=1,2;j=1,2,3,$$

where *MPG _{ij}* is the
response, miles per gallon, for cars made in decade

*i*at location

*j*. The treatment effects for the first factor, decade of manufacture, are the

*α*terms (constrained to sum to zero). The treatment effects for the second factor, location of manufacture, are the

_{i}*β*terms (constrained to sum to zero). The

_{j}*ε*are uncorrelated, normally distributed noise terms.

_{ij}The hypotheses to test are equality of decade effects,

$$\begin{array}{l}{H}_{0}:{\alpha}_{1}={\alpha}_{2}=0\\ {H}_{A}:at\text{\hspace{0.17em}}\text{\hspace{0.17em}}least\text{\hspace{0.17em}}\text{\hspace{0.17em}}one\text{\hspace{0.17em}}\text{\hspace{0.17em}}{\alpha}_{i}\ne 0,\end{array}$$

and equality of location effects,

$$\begin{array}{l}{H}_{0}:{\beta}_{1}={\beta}_{2}={\beta}_{3}=0\\ {H}_{A}:\text{\hspace{0.17em}}at\text{\hspace{0.17em}}\text{\hspace{0.17em}}least\text{\hspace{0.17em}}\text{\hspace{0.17em}}one\text{\hspace{0.17em}}\text{\hspace{0.17em}}{\beta}_{j}\ne 0.\end{array}$$

You can conduct a multiple-factor ANOVA using
`anovan`

.

anovan(MPG,{Decade,Location},'varnames',{'Decade','Location'});

This output shows the results of the two-way ANOVA. The p-value for testing
the equality of decade effects is `2.88503e-18`

, so the null
hypothesis is rejected at the 0.05 significance level. The p-value for testing
the equality of location effects is `7.40416e-10`

, so this null
hypothesis is also rejected.

### Conduct ANOCOVA analysis.

A potential confounder in this analysis is car weight. Cars with greater
weight are expected to have lower gas mileage. Include the variable
`Weight`

as a continuous covariate in the ANOVA; that is,
conduct an ANOCOVA analysis.

Assuming parallel lines, the statistical model is

$$MP{G}_{ijk}=\mu +{\alpha}_{i}+{\beta}_{j}+\gamma Weigh{t}_{ijk}+{\epsilon}_{ijk},\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}i=1,2;\text{\hspace{0.17em}}\text{\hspace{0.17em}}j=1,2,3;\text{\hspace{0.17em}}\text{\hspace{0.17em}}k=1,\mathrm{...},100.$$

The difference between this model and the two-way ANOVA model
is the inclusion of the continuous predictor,
*Weight _{ijk}*, the weight for the

*k*th car, which was made in the

*i*th decade and in the

*j*th location. The slope parameter is

*γ*.

Add the continuous covariate as a third group in the second
`anovan`

input argument. Use the name-value pair argument
`Continuous`

to specify that `Weight`

(the
third group) is continuous.

anovan(MPG,{Decade,Location,Weight},'Continuous',3,... 'varnames',{'Decade','Location','Weight'});

This output shows that when car weight is considered, there is insufficient
evidence of a manufacturing location effect (p-value =
`0.1044`

).

### Use interactive tool.

You can use the interactive `aoctool`

to explore this
result.

aoctool(Weight,MPG,Location);

This command opens three dialog boxes. In the ANOCOVA Prediction Plot dialog
box, select the **Separate Means** model.

This output shows that when you do not include `Weight`

in
the model, there are fairly large differences in the expected miles per gallon
among the three manufacturing locations. Note that here the model does not
adjust for the decade of manufacturing.

Now, select the **Parallel Lines** model.

When you include `Weight`

in the model, the difference in
expected miles per gallon among the three manufacturing locations is much
smaller.

## See Also

`nominal`

| `ordinal`

| `boxplot`

| `grpstats`

| `ttest2`

| `anovan`

| `aoctool`

## Related Examples

- Plot Data Grouped by Category
- Summary Statistics Grouped by Category
- Linear Regression with Categorical Covariates